Control 4 version of econometrics
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JOB control work Option 4
Task 1
1. Find the parameters of the linear regression equation using the method of least squares (OLS).
2. Write the equation of linear regression, using the matrix method.
3. Calculate the correlation coefficient and evaluate the resulting regression equation.
4. Calculate the coefficient of determination and assess the quality of a selected regression equation, these.
5. Calculate the average approximation error.
6. Evaluate the statistical significance of linear regression using the F-Fisher criterion.
7. Assess the statistical significance of the parameters of the linear regression equation (a and b), and correlation coefficient using the Student t-test.
8. Construct confidence intervals for the parameters of the linear regression equation (a and b) at a significance level α = 0,05.
Task 2
1. Create multiple linear regression equation y = a + b1x1 + b2x2 + ε in the matrix form, using OLS.
2. Construct a correlation matrix.
3. To assess the quality of the model by using the coefficient of determination R2.
Task 1
1. Find the parameters of the linear regression equation using the method of least squares (OLS).
2. Write the equation of linear regression, using the matrix method.
3. Calculate the correlation coefficient and evaluate the resulting regression equation.
4. Calculate the coefficient of determination and assess the quality of a selected regression equation, these.
5. Calculate the average approximation error.
6. Evaluate the statistical significance of linear regression using the F-Fisher criterion.
7. Assess the statistical significance of the parameters of the linear regression equation (a and b), and correlation coefficient using the Student t-test.
8. Construct confidence intervals for the parameters of the linear regression equation (a and b) at a significance level α = 0,05.
Task 2
1. Create multiple linear regression equation y = a + b1x1 + b2x2 + ε in the matrix form, using OLS.
2. Construct a correlation matrix.
3. To assess the quality of the model by using the coefficient of determination R2.
with very detailed calculations and tables. Tables are available in the demo version works
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